Application of Artificial Intelligence in Business

Contemporary Issues in Business & Management
APPLICATION OF ARTIFICIAL INTELLIGENCE IN BUSINESS
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Contemporary Issues in Business & Management 1
Introduction
Businesses have high expectations for artificial intelligence (AI). Mr. John McCarthy
who is referred to as the father of Artificial Intelligence defined AI as the science and
engineering focused on creating intelligent machines, particularly intelligent computer software.
In general, AI is the machine’s capacity to make intelligent decisions. AI is part of the digital
transformation that the world is undergoing. AI conventionally refers to a broad class of
technologies that allow a computer to perform tasks that normally require human cognition,
including adaptive decision making. This paper would be a discussion of the benefits and
challenges of implementing AI in the current business environment.
Implementation AI in The Current Business Environment
The 2016 report by McKinsey Global Institute stated that there were about $26 to $39
billion investments made to AI both from Tech giants and startups (Chui, 2017). These
estimated investments were from the 2013 results. While some industries such as healthcare are
falling behind due to regulatory challenges, other industries such as financing,
telecommunication, assembly, and automotive have started using AI (Chui, 2017).
Subsequently, despite being digital transformation ambitions for most businesses, most of
them have insufficient technology for providing the anticipated intelligence suitable (Agrawal,
Gans and Goldfarb, 2017). A significant outcome of digitalization initiatives is that most of the
companies are now collecting a large amount of data. The data collected are more of technology-
related such as production processing and internal machinery. Either, the data is more market-
based such as customer interaction. In this respect, business management is then faced with
multiple obstacles of extracting the required values from the massive volumes of data.

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One of the first successful implementations of AI was the 1997 IBM’s Deep Blue Chess
computer. This was the first computer to beat the World Chess Champion (Garry Kasparov)
(Greenemeier, 2017). The rule-abiding features of a chess game are considered to have been a
major challenge until Deep Blue broke it. More recently, IBM’s Watson engaged the former
winner of a televised quiz show and again, the computer’s ability to min both structured and
unstructured data sets helped Watson take the victory (Schneider and Gersting, 2015, p. 738).
Again, although it remained contested, Eugene Goostman a chatbot managed to convince one-
third of the judges that it was a real human being in 2014 Turing test (Schneider and Gersting,
2015, p. 710). The achievements which were shown by Watson, Deep Blue, and Eugene have
informed the development of AI to an extent that machines are projected to human intelligence.
For instance, the exponential upsurge in the AI complexities and effectiveness in the gaming
industry has made computers to learn human behaviors, respond to their stimuli and even reply
in other ways that humans cannot predict. According to McKinsey as reported in (Havard
Business Review, 2019), AI was projected to create USD13 trillion of GDP growth by 2030
which most of it will come from non-internet sectors such as agriculture, manufacturing, energy,
education and logistics.
The implementation of AI is centered towards the use of natural language processing
(NLP), AI semantic reasoning (Semantic AI), machine learning (ML), and intelligent processing
and retrieval (IDPR)of data (IDPR) (Tredinnick, 2017). NLP is a concept in AI that focuses on
empowering computers to decipher, comprehend, and process human language. This is the first
step of technology in getting machines closer to a human level of understanding and reasoning.
In businesses, NLP has been used in various ways. In healthcare, the use of chatbots, knowledge
management and analysis of sentiment analysis has become fundamental tools in the assessment

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of patients to improve care. In a study by (Short et al., 2010), the authors found that textual
analysis using NLP can bring advantage can enable businesses and entrepreneurs research,
compared respondents’ responses, and analyze their willingness to answer questionnaires. In the
constructions industry, construction businesses spend lots of money for hospitalization of injured
workers, and compensation for torts of liabilities for the injuries. While demonstrating the
application of AI in the reduction of injuries, the study of (Tixier et al., 2016) showed that NLP
and ML can help construction companies to predict construction-related injuries.
In the manufacturing and construction industries, the implementation of AI is helping
businesses reduce the risks. In a study conducted by (Prasath Kumar, Balasubramanian and
Jagadish Raj, 2016), the authors intended to analyze the efficiency practicality of using robotics
and automation in construction. By adopting the procedures of value estimation, return on
investment, payback period, straight line methods on each robot, the study showed that
automation in construction industry reduced the average required time by 57.85%. operation
costs reduced by 51.67%, and the cost of rework reduced by 66.76% when compared to manual
work. Apart from cost reduction, AI in robotics has been shown to attain jobs that could have
been dangerous for humans. The work of (West, 2015) states that automated equipment could
work in places with disastrous scenes such as nuclear reactors which are considered too
dangerous for humans operations.
In another study of (Ullah et al., 2016), the authors used NLP to analyze customer
emotions using product review provided online. The study found that customers had different
emotions regarding where the difference diminished as the product remained in the market. This
information is important business management as businesses can study customer emotions when
they use a product after it has been launched, and then use the reviews to improve the product.